Abstract
Many computer applications take advantage of computer-generated numeric sequences having properties very similar to truly random variables. Sequences generated by computer algorithms through mathematical operations are not really random, having no intrinsic unpredictability, and are necessarily deterministic and reproducible.
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Notes
- 1.
In realistic cases of finite numeric precision, one of the extreme values is excluded. It would have a corresponding zero probability, in the case of infinite precision, but it is not the case with finite machine precision.
References
R. May, Simple mathematical models with very complicated dynamics. Nature 621, 459 (1976)
Logistic map. public domain image, 2011. https://commons.wikimedia.org/wiki/File:LogisticMap_BifurcationDiagram.png
T.O. Group, The single UNIX ®; specification (1997). http://www.unix.org. Version 2
T.G. project, GNU operating system—GSL—GNU scientific library (1996–2011), http://www.gnu.org/software/gsl/
M. Lüscher, A portable high-quality random number generator for lattice field theory simulations. Comput. Phys. Commun. 79, 100–110 (1994)
F. James, Ranlux: A fortran implementation of the high-quality pseudorandom number generator of Lüscher. Comput. Phys. Commun. 79, 111–114 (1994)
P. L’Ecuyer, Maximally equidistributed combined Tausworthe generators. Math. Comput. 65, 203–213 (1996)
M. Matsumoto, T. Nishimura, Mersenne twistor: a 623-dimensionally equidistributed unifor pseudorandom number generator. ACM Trans. Model. Comput. Simul. 8, 3–30 (1998)
G.E.P. Box, M. Muller, A note on the generation of random normal deviates. Ann. Math. Stat. 29, 610–611 (1958)
G. Marsglia, W. Tsang, The Ziggurat method for generating random variables. J. Stat. Softw. 5, 8 (2000)
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Lista, L. (2016). Random Numbers and Monte Carlo Methods. In: Statistical Methods for Data Analysis in Particle Physics. Lecture Notes in Physics, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-319-20176-4_4
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